Comment continued: Notice that the small sample here is very much smaller than you
are suggesting in your Question.

Demonstration of K-W test where one level has **_very_** small sample size.

With sample sizes 4, 200, 100, and a difference of 15 between population
group means, the K-W test does not give a significant result

    set.seed(1121)
    x1 = rnorm(4, 95, 10)
    x2 = rnorm(200, 110, 10)
    x3 = rnorm(100, 110, 10)
    x = c(x1,x2,x3)
    g = rep(1:3, times=c(4,200,100))
    kruskal.test(x ~ g)

            Kruskal-Wallis rank sum test

    data:  x by g
    Kruskal-Wallis chi-squared = 5.2646, df = 2,  
     p-value = 0.07191

In a similar situation with 100 observations at each level, the
difference is found to be very highly significant.

    set.seed(1122)
    x1 = rnorm(100, 95, 10)
    x2 = rnorm(100, 110, 10)
    x3 = rnorm(100, 110, 10)
    x = c(x1,x2,x3)
    g = rep(1:3, each=100)
    kruskal.test(x ~ g)

            Kruskal-Wallis rank sum test

    data:  x by g
    Kruskal-Wallis chi-squared = 97.804, df = 2, 
     p-value < 2.2e-16

Notches in the boxplots below are nonparametric confidence intervals
calibrated so that, roughly speaking, non-overlapping CIs suggest
a difference in location between two levels. (In the first example, it
would be problematic to make a boxplot with only 4 observations at the first
level.)

    boxplot(x ~ g, col="skyblue2", notch=T)

[![enter image description here][1]][1]


  [1]: https://i.sstatic.net/qR8lo.png